Statistical and Machine Learning Approaches for Network Analysis (notice n° 61389)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 02568cam a2200289zu 4500 |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | FRCYB88813009 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20250107211251.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 250107s2012 fr | o|||||0|0|||eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9780470195154 |
| 035 ## - SYSTEM CONTROL NUMBER | |
| System control number | FRCYB88813009 |
| 040 ## - CATALOGING SOURCE | |
| Original cataloging agency | FR-PaCSA |
| Language of cataloging | en |
| Transcribing agency | |
| Description conventions | rda |
| 100 1# - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Dehmer, Matthias |
| 245 01 - TITLE STATEMENT | |
| Title | Statistical and Machine Learning Approaches for Network Analysis |
| Statement of responsibility, etc. | ['Dehmer, Matthias', 'Basak, Subhash C. '] |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
| Name of producer, publisher, distributor, manufacturer | John Wiley & Sons |
| Date of production, publication, distribution, manufacture, or copyright notice | 2012 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | p. |
| 336 ## - CONTENT TYPE | |
| Content type code | txt |
| Source | rdacontent |
| 337 ## - MEDIA TYPE | |
| Media type code | c |
| Source | rdamdedia |
| 338 ## - CARRIER TYPE | |
| Carrier type code | c |
| Source | rdacarrier |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | Explore the multidisciplinary nature of complex networks through machine learning techniques Statistical and Machine Learning Approaches for Network Analysis provides an accessible framework for structurally analyzing graphs by bringing together known and novel approaches on graph classes and graph measures for classification. By providing different approaches based on experimental data, the book uniquely sets itself apart from the current literature by exploring the application of machine learning techniques to various types of complex networks. Comprised of chapters written by internationally renowned researchers in the field of interdisciplinary network theory, the book presents current and classical methods to analyze networks statistically. Methods from machine learning, data mining, and information theory are strongly emphasized throughout. Real data sets are used to showcase the discussed methods and topics, which include: A survey of computational approaches to reconstruct and partition biological networksAn introduction to complex networks—measures, statistical properties, and modelsModeling for evolving biological networksThe structure of an evolving random bipartite graphDensity-based enumeration in structured dataHyponym extraction employing a weighted graph kernel Statistical and Machine Learning Approaches for Network Analysis is an excellent supplemental text for graduate-level, cross-disciplinary courses in applied discrete mathematics, bioinformatics, pattern recognition, and computer science. The book is also a valuable reference for researchers and practitioners in the fields of applied discrete mathematics, machine learning, data mining, and biostatistics. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | |
| 700 0# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Dehmer, Matthias |
| 700 0# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Basak, Subhash C. |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Access method | Cyberlibris |
| Uniform Resource Identifier | <a href="https://international.scholarvox.com/netsen/book/88813009">https://international.scholarvox.com/netsen/book/88813009</a> |
| Electronic format type | text/html |
| Host name | |
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